Tuesday, April 29, 2014

There is now ample evidence
that warming temperatures cause advances in the timing of organismal activity
(i.e., phenology). Studies have shown that rising temperatures are responsible
for earlier plant leafing and flowering (Miller-Rushing & Primack 2008,
Wolkovich et al. 2012), pest insect emergence and abundance (Willis et al.
2008), and even local species loss and reduced diversity (Willis et al. 2008).
One emerging expectation from global warming studies is that insects should
emerge earlier since winters are milder and spring temperatures are warmer. This
expectation should hold so long as high temperatures or other environmental
stressors don’t adversely affect the insects. And the concern about shifts in emergence
and insect activity is the potential for mismatches between plant flowering and
the availability of pollinators (Willmer 2012) –if insects emerge too soon,
they may miss the flowers.

Photo by Marc Cadotte

In a forthcoming paper in Ecology by Sarah Diamond and colleagues study 20 common butterfly species across more than 80 sites in Ohio. These sites were located in a range of places across a rural to urban gradient. Instead of finding earlier emergence in warmer places, which were typically urban areas, they found that a number of species were delayed in warmer urban areas. Even though the butterflies might emerge earlier in warmer rural habitats, they were adversely affected in urbanized areas.

These results highlight the need to consider multiple sources of stress from different types of environmental change. Observations from a few locales or from controlled experiments may not lead to conclusions about interactive influences or warming and urbanization, and that's why this study is so important. It observes a counter-intuitive result because of the influence of multiple stressors.

A next step should be to determine if pollinator-plant interactions are being disrupted in these urban areas. The reason why we should care so much about pollinator emergence is that they provide a key ecological service by pollinator wild, garden, and agricultural plants, as well has being an important food source to other species. A mismatch in timing and disrupt these important interactions.

Thursday, April 24, 2014

I’m sitting in the Sydney airport waiting for my delayed
flight –which gives me some time to ruminate about the mini-conference I am
leaving. The conference, hosted by the Centre for Biodiversity Analysis (CBA)
and CSIRO
in Australia, on "Understanding biodiversity dynamics using diverse data sources", brought together several fascinating thinkers working on disparate areas including ecology, macroecology, evolution, genomics, and computer science. The goal of the conference was to see if merging different forms of data could lead to greater insights into biodiversity patterns and processes.

Happy integration

On the surface, it seems uncontroversial to say that bringing together different forms of data really does promote new insights into nature. However, this only really works if the data we combine meaningfully complement one another. When researchers bring together data, there are under-appreciated risks, and the resulting effort could result in trying to combine data that make weird bedfellows.

Weird bedfellows

The risks include data that mismatch in the scale of observation, resulting in meaningful variation being missed. Data are often generated according to certain models with specific assumptions, and these data-generation steps can be misunderstood by end-users, resulting in inappropriate uses of data. Further, different data may be combined in standard statistical models, but the linkages between data types is much more subtle and nuanced, requiring alternative models.

Why these are issues stems from the fact that researchers now have an unprecedented access to numerous large data sets. Whether these are large trait data sets, spatial locations, spatial environmental data, genomes, or historical data, they are all built with specific underlying uses, limitations and assumptions.

Regardless of these issues of concern, the opportunity and power to address new questions is greatly enhanced by multiple types of data. One thing I gained from this meeting is that there is a new world of biodiversity analysis and understanding emerging by smart people doing smart things with multiple data. We will soon live in a world where the data and analytical tools allow research to truly combine multiple processes to predict species' distributions, or to move from evolutionary events in deep history to modern day ecological patterns.

Wednesday, April 23, 2014

The first in a series of guest posts about using scientific teaching, active learning, and flipping the classroom by Sarah Seiter, a teaching fellow at the University of Colorado, Boulder.

As a faculty member teaching can sometimes seem like a chore – your lectures compete with smartphones and laptops. Some students see themselves as education “consumers” and haggle over grades. STEM (science, technology, engineering, and math) faculty have a particularly tough gig – students need substantial background to succeed in these courses, and often arrive in the classroom unprepared. Yet, the current classroom climate doesn’t seem to be working for students either. About half of STEM college majors ultimately switch to a non-scientific field. It would be easy to frame the problem as one of culture – and we do live in a society that doesn’t always value science or education. However, the problem of reforming STEM education might not take social change, but rather could be solved using our own scientific training. In the past few years a movement called “scientific teaching” has emerged, which uses quantitative research skills to make the classroom experience better for instructors as well as students.

So how can you use your research skills to boost your teaching? First, you can use teaching techniques that have been empirically tested and rigorously studied, especially a set of techniques called “active learning”. Second, you can collect data on yourself and your students to gauge your progress and adjust your teaching as needed, a process called “formative assessment”. While this can seem daunting, it helps to remember that as a researcher you’re uniquely equipped to overhaul your teaching, using the skills you already rely on in the lab and the field. Like a lot of paradigm shifts in science, using data to guide your teaching seems pretty obvious after the fact, but it can be revolutionary for you and your students.

What is Active Learning:

There are a lot of definitions of active learning floating around, but in short active learning techniques force students to engage with the material, while it is being taught. More importantly, students practice the material and make mistakes while they are surrounded by a community of peers and instructors who can help. There are a lot of ways to bring active learning strategies to your classroom, such as clicker response systems (handheld devices that allow them to take short quizzes throughout the lecture). Case studies are another tool: students read about scientific problems and then apply the information to real world problems (medical and law schools have been them for years). I’ll get into some more examples of these techniques in post II; there are lots of free and awesome resources that will allow you to try active learning techniques in your class with minimal investment.

Formative Assessment:

The other way data can help you overhaul your class is through formative assessment, a series of small, frequent, low stakes assessment of student learning. A lot of college courses use what’s called summative assessment – one or two major exams that test a semester’s worth of material, with a few labs or a term paper for balance. If your goal is to see if your students learned anything over a semester this is probably sufficient. This is also fine if you’re trying to weed out underperforming students from your major (but seriously, don’t do that). But if you’re interested in coaching students towards mastery of the subject matter, it probably isn’t enough to just tell them how much they learned after half the class is over. If you think about learning goals like we think of fitness goals, this is like asking students to qualify for the Boston marathon, without giving them any times for their training runs.

Formative assessment can be done in many ways: weekly quizzes or taking data with classroom clicker systems. While a lot of formative assessment research focuses on measuring student progress, instructors have lots to gain by measuring their own pedagogical skills. There are a lot of tools out there to measure improvement in teaching skills (K-12 teachers have been getting formatively assessed for years), but even setting simple goals for yourself (“make at least 5 minutes for student questions”) and monitoring your progress can be really helpful. Post III will talk about how to do (relatively) painless formative assessment in your class.

How does this work and who does it work for:

Scientific teaching is revolutionary because it works for everyone, faculty and students alike. However, it has particularly useful benefits for some types of instructors and students.

New Faculty: inexperienced faculty can achieve results as good or better than experienced faculty by using evidence based teaching techniques. In a study at the University of Colorado, physics students taught by a graduate TA using scientific teaching outperformed those taught by an experienced (and well loved) professor using a standard lecture style (you can read the study here). Faculty who are not native English speakers, or who are simply shy can get a lot of leverage using scientific teaching techniques, because doing in-class activities relieves the pressure to deliver perfect lectures.

Test scores between a lecture-taught physics section
and a section taught using active learning techniques.

Seasoned Faculty: For faculty who already have their teaching style established, scientific teaching can spice up lectures that have become rote or help you address concepts that you see students struggle with year after year. Even if you feel like you have your lectures completely dialed in, consider whether you’re using the most cutting edge techniques in your lab, and if you your classroom deserves the same treatment.

Students also stand to gain from scientific teaching, and some groups of students are particularly poised to benefit from it:
Students who don’t plan to go into science: Even in majors classes, most of the students we teach won’t go on to become scientists. But skills like analyzing data, and writing convincing evidence based arguments are useful in almost any field. Active learning trains students to be smart consumers of information, and formative assessment teaches students to monitor their own learning – two skills we could stand to see more of in any career.

Students Who Love Science: Active learning can give star students a leg up on the skills they’ll need to succeed as academics, for all the reasons listed above. Occasionally really bright students will balk at active learning, because having to wrestle with complicated data makes them feel stupid. While it can feel awful to watch your smartest students struggle, it is important to remember that real scientists have to confront confusing data every day. For students who want research careers, learning to persevere through messy and inconclusive results is critical.

Students who struggle with science: Active learning can be a great leveler for students who come from disadvantaged backgrounds. A University of Washington study showed that active learning and student peer tutoring could eliminate achievement gaps for minority students. If you partially got into academia because you wanted to make a difference in educating young people, here is one empirically proven way to do that.

Content Isn’t King Anymore: Taking time to work with data, or apply scientific research to policy problems takes more time, so instructors can cover fewer examples in class. In active learning, students are developing scientific skills like experimental design or technical writing, but after spending an hour hammering out an experiment to test the evolution of virulence, they often feel like they’ve only learned about “one stupid disease”. However, there is lots of evidence that covering topics in depth is more beneficial than doing a survey of many topics. For example, high schoolers that studied a single subject in depth for more than a month were more likely to declare a science major in college than students who covered more topics.

Demands on Instructor Time: I actually haven’t found that active learning takes more time to prepare –case studies and clickers actually take a up a decent amount of class time, so I spend less time prepping and rehearsing lectures. However, if you already have a slide deck you’ve been using for years, developing clicker questions and class exercises requires an upfront investment of time. Formative assessment can also take more time, although online quiz tools and peer grading can help take some of the pressure off instructors.

If you want to learn more about the theory behind scientific teaching there are a lot of great resources on the subject:

Null models have become a fundamental part of community ecology. For the most part, this is an improvement over our null-model free days: patterns are now interpreted with reference to patterns that might arise through chance and in the absence of ecological processes of interest. Null models today are ubiquitous in tests of phylogenetic signals, patterns of species co-occurrence, models of species distribution-climate relationships. But even though null models are a success in that they are widespread and commonly used, there are problems--in particular, there is a disconnect between how null models are chosen and interpreted and what information they actually provide. Unfortunately, simple and easily applied null models tend to be favoured, but they are often interpreted as though they are complicated, mechanism-explicit models.

The new paper “Neutral Biogeography and the Evolution of Climatic Niches” from Boucher et al. provides a good example of this problem. The premise of the paper is straightforward: studies of phylogenetic niche conservation tend to rely on simple null models, and as a result may misinterpret what their data shows because of the type of null models that they use. The study of phylogenetic niche conservation and niche evolution is becoming increasingly popular, particularly studies on how species' climatic niches evolve and how climate niches relate to patterns of diversity. In a time of changing climates, there are also important applications looking at how species respond to climatic shifts. Studies of changes in climate niches through evolutionary time usually rely on a definition of the climate niche based on empirical data, more specifically, the mean position of a given species along a continuous abiotic gradient. Because this is not directly tied to physiological measurements, climate niche data may also capture the effect of dispersal limitations or biotic interactions. Hence the need for null models, however the null models used in these studies primarily flag changes in climate niche that result from to random drift or selection in a varying environment. These types of null models use Brownian motion (a "random walk") to answer questions about whether niches are more or less similar than expected due to chance, or else whether a particular model of niche evolution is a better fit to the data than a model of Brownian motion.

The authors suggest that the reliance on Brownian motion is problematic, since these simple null models cannot actually distinguish between patterns of climate niches that arise simply due to speciation and migration but no selection on climate niches, and those that are the result of true niche evolution. If this is true, conclusions about niche evolution may be suspect, since they depend on the null model used. The authors used a neutral, spatially explicit model (known as an "alternative neutral biogeographic model") that simulates dynamics driven only by speciation and migration, with species being neutral in their dynamics. This provides an alternative model of patterns that may arise in climate niches among species, despite the absence of direct selection on the trait. The paper then looks at whether climatic niches exhibit phylogenetic signals when they arise via neutral spatial dynamics; if gradualism a reasonable neutral expectation for the evolution of climatic niches on geological timescales; and whether constraints on climatic niche diversification can arise simply through bounded geographic space. Simulations of the neutral biogeographic model used a gridded “continent” with variable climate conditions: each cell has a carrying capacity, and species move via migration and split into two species either by point mutation, or else by vicariance (a geographic barrier appears, leading to divergence of 2 populations). Not surprisingly, their results show that even in the absence of any selection on species’ climate niches, patterns can result that differ greatly from a simple Brownian motion-based null model. So the simple null model (Brownian motion) often concluded that results from the more complex null model were different from the random/null expectation. This isn't a problem per se. The problem is that currently interpretations of the Brownian motion model may be that anything different from null is a signal for niche evolution (or conservation). Obviously that is not correct.

This paper is focused on the issue of choosing null models for studies of climate niche evolution, but it fits into a current of thought about the problems with how ecologists are using null models. It is one thing to know that you need and want to use a null model, but it is much more difficult to construct an appropriate null model, and interpret the output correctly. Null models (such as the Brownian motion null model) are often so simplistic that they are straw man arguments – if ecology isn't the result of only randomness, your null model is pretty likely to be a poor fit to the data. On the other hand, the more specific and complex the null model is, the easier it is to throw the baby out with the bathwater. Given how much data is interpreted in the light of null models, it seems that choosing and interpreting null models needs to be more of a priority.

Thursday, April 3, 2014

A few interesting links, especially about the dangers of when one aspect of science, data analysis, or knowledge receives inordinate focus.

A new article in Bioscience repeats the fear that natural history is losing its place in science, and that natural history's contributions to science have been devalued. "Natural history's place in science and society" makes some good points as to the many contributions that natural history has made to science, and it is fairly clear that natural history is given less and less value within academia. As always though, the issue is finding a ways to value useful natural history contributions (museum and herbarium collections, Genbank contributions, expeditions, citizen science) in a time of limited funds and (over)emphasis on the publication treadmill. Nature offers its take here, as well.

An interesting opinion piece on how the obsession with quantification and statistics can go too far, particularly in the absence of careful interpretation. "Can empiricism go too far?"

And similarly, does Big Data have big problems? Though focused on applications for the social sciences, there are some interesting points about the space between "social scientists who aren’t computationally talented and computer scientists who aren’t social-scientifically talented", and again, the need for careful interpretation. "Big data, big problems?"